Association between comorbidity clusters and mortality in patients with cancer: A machine learning analysis of data from the US National Health and Nutrition Examination Survey 1999–2018.

Abstract

11132 Background: Multimorbidity is common among patients with cancer; however, research on cancer prognosis has predominantly focused on cancers in isolation. This study identified comorbidity clusters among patients with cancer using machine learning and examine their associations with survival outcomes in a nationally representative sample of the US. Methods: This study used 10 survey cycles of the National Health and Nutrition Examination Survey from 1999 to 2018. Participants aged ≥20 years with a self-reported history of cancer were included. Comorbidities were ascertained through self-reports and quantitative measurements. Machine learning techniques, including Bernoulli mixture model and partition-based methods, were used to identify comorbidity clusters. Cox proportional hazards models were used to analyze the associations between comorbidity clusters and mortality outcomes, including all-cause mortality and cause-specific mortality (cancer, cardiovascular disease [CVD], and respiratory diseases), adjusting for relevant covariates. Results: The study included 4,390 participants. Four comorbidity clusters were identified: Low Comorbidity, Metabolic, CVD, and Respiratory. After adjusting for confounders, participants in the Respiratory Cluster had the highest risk of all-cause mortality (adjusted hazard ratio[aHR]=1.62, 95% confidence interval [CI]=1.26-2.08, p<0.001), followed by the CVD Cluster (aHR=1.50, 95%CI = 1.26-1.80, p<0.001) and the Metabolic Cluster (aHR=1.15, 95%CI=1.02-1.29, p=0.03) compared to the Low Comorbidity Cluster. The Metabolic, CVD, and Respiratory clusters were associated with higher risks of cardiovascular disease-related mortality (aHR=1.48-3.05, p<0.003), but no significant differences in cancer mortality were observed among the clusters. The effects of comorbidity clusters on all-cause mortality were modified by income-to-poverty ratio ( p for interaction=0.04), diet quality ( p for interaction=0.02), time since cancer diagnosis ( p for interaction=0.009), and cancer prognosis ( p for interaction=0.005). Conclusions: High comorbidity burden clusters showed increased all-cause and CVD-related mortality. Moreover, diet quality and socioeconomic status modified these associations. Machine learning approaches can provide valuable insights into complex multimorbidity profiles in patients with cancer. Further research is needed to deepen understanding of the relationships between multimorbidity, mortality, and cancer-specific outcomes.